通过机器学习范式的镜看待计算生物学中的Omics数据集成
Aziz Fouché1,2,3,4, Andrei Zinovyev5
1Institut Curie, PSL Research University, Paris, France.
Frontiers in bioinformatics
|August 21, 2023
概括
生物数据集成结合了多样化的数据集,以获得更深入的见解. 本综述涵盖了将复杂的生物数据集成为先进的计算生物学分析的方法,挑战和陷.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 数据科学数据科学数据科学
背景情况:
- 大规模生物数据的获取正在增加,使得更深层次的生物问题成为可能.
- 数据复杂性来自于不同的数据类型和批量效应,阻碍了联合分析.
- 数据集成旨在为多个数据集创建统一的表示.
研究的目的:
- 审查常见的生物数据类型,并定义数据整合问题.
- 为有效的数据集成提供基于机器学习的方法.
- 讨论生物数据整合当前的局面,陷和未来的挑战.
主要方法:
- 对数据整合方法的系统审查.
- 机器学习应用在数据集成中的解释.
- 讨论常见的数据类型和整合挑战.
主要成果:
- 现有的数据集成范式和算法的概述.
- 识别使用数据整合工具的陷.
- 阐明未来的研究方向和挑战.
结论:
- 数据整合对于利用复杂的生物数据集至关重要.
- 机器学习已经显著提升了数据集成能力.
- 应对当前的挑战是该领域未来进步的关键.
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